Artificial Intelligence. Machine Learning. Natural Language Processing.
Artificial intelligence. Machine learning. Natural language processing. Once the stuff of science fiction, these high-tech unions of human intelligence and computing capabilities, of algorithms and efficiencies, have already transformed business information technology, and they are poised for even greater growth in the future.
With drastic changes in information processing compounding the ever-growing volumes of information being produced, it is critical for digital strategists to develop a working knowledge of key concepts and terminology.
Read on for a glossary of key terms, along with links to further reading and resources.
Artificial intelligence (AI) is something of a blanket concept under which many other key concepts in business information technology are categorized. It may even be fair to say that AI is the core of business intelligence today.
Simply put, AI is a branch of computer science that strives to create intelligent machines capable of working and reacting like humans. AI research is diverse and specialized, but core problems and goals in the field include knowledge, reasoning, problem solving, perception, learning, planning, speech recognition, natural language processing, and the ability to manipulate and move objects.
From virtual assistants like Apple’s Siri and Microsoft’s Cortana, to credit card fraud detection systems, to gaming systems with motion-detection technology, to online customer support staffed by “chat bots,” AI applications have already begun to permeate daily life in work and play. One core area of AI, machine learning, is making huge strides in business information; machine learning is discussed in more detail below.
For further reading:
Techopedia defines and describes AI. Computerworld explains strong AI, weak AI, and the difference between “narrow” and “general” artificial intelligence. The Association for the Advancement of Artificial Intelligence (AAAI) maintains a digital library of articles, reports and conference papers on a variety of AI topics. Wikipedia offers a thorough overview of artificial intelligence and a glossary of AI terms.
Machine learning is a type of artificial intelligence that allows computers to “learn” (respond to new challenges, improve their own performance, grow and change) without being explicitly programmed. Machine learning relies heavily on pattern and trend recognition.
Machine learning can be used to personalize and customize products. Facebook’s NewsFeed, for example, employs machine learning by changing the content displayed in response to user input (which posts are tagged, how often comments are posted to certain friends’ walls, etc.). Netflix recommendations are another common example. As the Netflix algorithms learn what users watch, like and add to their wishlists, the list of recommended shows and movies can be changed and updated according to the preferences of every account holder. As more and more products and services employ machine learning technologies, customers will become accustomed to personalized experiences, and businesses will need to keep pace with both technology and user expectations.
For further reading:
TechTarget explains the use of algorithms and analytics in machine learning, as well as how the field differs from data mining. Techopedia’s definition provides examples and describes how machine learning differs from human intuition in some key ways. SAS Industries offers an excellent discussion of machine learning, including its common uses and place in today’s environment.
Natural Language Processing
Natural language processing, or NLP, is a subfield of both AI and machine learning. NLP is essentially a translation process between human and machine, involving a computer’s ability to understand human speech as it is spoken. In NLP, computers respond to the way humans learn and use language, rather than requiring language that conforms to highly structured, rigid or limited rules. Instead of inputting a command that must be formatted according to the rules of a programming language, for example, a person using an NLP-enabled program could speak or type naturally.
The development of NLP is very challenging, both because human language is very complex, and because variations in dialect, accent, slang and context further complicate linguistic analysis. The ultimate goal of NLP, though, is seamless communication that allows a user to interact (“speak”) with a computer as if the machine were another human.
NLP is already well established and continues to grow. Voice recognition, speech-to-text and translation are widespread, both individually and in combination. Consider, for example, the Google Translate app, which allows users to type or speak a word or phrase in a chosen language, and then receive a written and spoken translation in return. Dictated smartphone texts allow hands-free communication of business and personal information. Though imperfect and at times a source of unintended humor, these technologies are the tip of what is likely to be an NLP iceberg.
For further reading:
TechTarget and Techopedia offer clear definitions with examples. KDNuggets explains the incorporation of NLP tasks into software programs and lists potential applications of NLP technology. Dataversity explains the role of NLP in business intelligence, social media analysis and more.
Other Terms to Know
In addition to the three key concepts listed above, several other, related terms come into play. These complex applications involve combinations of AI, machine learning and more, and illustrate the range of current and future possibilities for business IT.
Deep learning is a type of machine learning that involves building and training artificial neural networks. As Forbes explains, “Deep learning carries out the machine learning process using an artificial neural net that is composed of a number of levels arranged in a hierarchy. The network learns something simple at the initial level in the hierarchy and then sends this information to the next level. …This process continues as each level in the hierarchy builds something more complex from the input it received from the previous level.” Deep learning has gotten increased attention recently due to Google’s keen interest, including the Google Brain Team, acquisition of DeepMind, and the Google DeepMind AlphaGo project. For more information on deep learning, see O’Reilly and MIT Technology Review.
Cognitive computing uses computer hardware and/or software to mimic the functions of the human brain. Cognitive computing uses a variety of AI applications including machine learning algorithms, natural language processing and neural networks. IBM’s Watson is perhaps the best-known cognitive system, and IBM remains a leader in the field, with other cognitive computing projects and a white paper on key developments. For further reading, Forbes and the Cognitive Computing Consortium provide additional information about this important aspect of AI.
Predictive analytics uses data, statistics and machine learning techniques to make predictions about future events. Existing data sets are used to identify trends and patterns, and then to predict future trends or outcomes. NGData explains that, while not an exact science, predictive analytics has value in business intelligence because it “does provide companies with the ability to reliably forecast future trends and behaviors.” Predictive analytics has many useful applications, including customer relationship management, health care, insurance underwriting, finance and collections, fraud detection, risk management, and marketing. For further information, SAS and Harvard Business Review offer detailed discussion.
As the flow of new data continues unabated and technological advancements progress further each day, it is clear that organizations must employ careful, informed strategies to remain competitive. A strong understanding of key trends and concepts in business information technology is a critical first step, allowing information professionals and other business leaders to be engaged and prepared as they embrace new trends and technologies.
Tame the Ever-Increasing Flow of Information
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